AI Precipitation and Temperature Forecasting

Automated retrieval of initial conditions from global operational sources, an adapter layer that harmonises variable names, grids and physical definitions across sources, and computation of variables some models need that open sources do not provide.

The current generation of neural weather models, among them GraphCast, FourCastNet, Pangu-Weather, Aurora and FuXi, runs in a single reproducible environment with the operational numerical model as a reference baseline. A ten-day global forecast runs in seconds on a single GPU.

An evaluation framework scores every model for the target territory by region, month, pressure level and lead time, in two regimes: operational initial conditions and clean reanalysis.

A neural network takes the context and outputs the weight of each model, trained to minimise the combined forecast error. Weights sum to one, and a rule-based representation keeps the weighting interpretable for meteorologists.

An encoder-processor-decoder graph network maps the ensemble grid to the territory's observation stations and learns the remaining systematic bias, with edges that connect stations by proximity, elevation difference and climatic similarity.

Temporal hold-out, spatial hold-out and their combination, with deterministic, anomaly-correlation and probabilistic metrics, so the platform is trusted where no station exists.

A map-based platform with forecast, current-state and history modes, comparison against the operational baseline, vector wind fields as particle flow, probabilistic output with uncertainty, and a request-driven backend.

Extending the station layer from surface variables to upper-air fields such as geopotential height and wind.